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The powerful discriminative ability of deep convolutions in deep learning-based BCD methods has considerably increased the accuracy and efficiency. However, dense and continuously distributed buildings contain a wide range of multi-scale features, which render current deep learning methods incapable of discriminating and incorporating multiple features effectively. In this work, we propose a Siamese cross-attention discrimination network (SCADNet) to identify complex information in bitemporal images and improve the change detection accuracy. Specifically, we first use the Siamese cross-attention (SCA) module to learn unchanged and changed feature information, combining multi-head cross-attention to improve the global validity of high-level semantic information. Second, we adapt a multi-scale feature fusion (MFF) module to integrate embedded tokens with context-rich channel transformer outputs. Then, upsampling is performed to fuse the extracted multi-scale information content to recover the original image information to the maximum extent. For information content with a large difference in contextual semantics, we perform filtering using a differential context discrimination (DCD) module, which can help the network to avoid pseudo-change occurrences. The experimental results show that the present SCADNet is able to achieve a significant change detection performance in terms of three public BCD datasets (LEVIR-CD, SYSU-CD, and WHU-CD). For these three datasets, we obtain F1 scores of 90.32%, 81.79%, and 88.62%, as well as OA values of 97.98%, 91.23%, and 98.88%, respectively.<\/jats:p>","DOI":"10.3390\/rs14246213","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T03:23:49Z","timestamp":1670556229000},"page":"6213","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["SCAD: A Siamese Cross-Attention Discrimination Network for Bitemporal Building Change Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7099-7833","authenticated-orcid":false,"given":"Chuan","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2193-9124","authenticated-orcid":false,"given":"Zhaoyi","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Liye","family":"Mei","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"},{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}]},{"given":"Sen","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Weapon Engineering, Naval Engineering University, Wuhan 430032, China"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Haigang","family":"Sui","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}]},{"given":"Wei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China"}]},{"given":"Shaohua","family":"Sun","sequence":"additional","affiliation":[{"name":"Air Force Research Academy, Beijing 10085, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2016.09.013","article-title":"3D change detection\u2014Approaches and applications","volume":"122","author":"Qin","year":"2016","journal-title":"ISPRS J. 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